Overview

Dataset statistics

Number of variables12
Number of observations64150
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.9 MiB
Average record size in memory96.0 B

Variable types

DateTime1
Numeric11

Warnings

GAES_GEN has 42114 (65.6%) zeros Zeros
GAES_PUMP has 44839 (69.9%) zeros Zeros

Reproduction

Analysis started2021-05-02 12:51:29.913120
Analysis finished2021-05-02 12:51:53.241136
Duration23.33 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

Distinct64149
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size501.3 KiB
Minimum2014-01-01 01:00:00
Maximum2021-04-28 07:00:00
2021-05-02T15:51:53.320487image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:53.442812image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

AES
Real number (ℝ≥0)

Distinct5342
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9561.336352
Minimum6014
Maximum12724
Zeros0
Zeros (%)0.0%
Memory size501.3 KiB
2021-05-02T15:51:53.566527image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum6014
5-th percentile7448
Q18485
median9643
Q310591
95-th percentile11553
Maximum12724
Range6710
Interquartile range (IQR)2106

Descriptive statistics

Standard deviation1283.710613
Coefficient of variation (CV)0.1342605851
Kurtosis-0.9843520169
Mean9561.336352
Median Absolute Deviation (MAD)1055
Skewness-0.1627758884
Sum613359727
Variance1647912.938
MonotocityNot monotonic
2021-05-02T15:51:53.685451image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1054250
 
0.1%
953349
 
0.1%
1073749
 
0.1%
1075647
 
0.1%
1053147
 
0.1%
1075446
 
0.1%
1053846
 
0.1%
1074745
 
0.1%
1097344
 
0.1%
1055144
 
0.1%
Other values (5332)63683
99.3%
ValueCountFrequency (%)
60141
< 0.1%
62491
< 0.1%
63081
< 0.1%
63141
< 0.1%
63411
< 0.1%
ValueCountFrequency (%)
127242
< 0.1%
127121
< 0.1%
126412
< 0.1%
126391
< 0.1%
126382
< 0.1%

TEC
Real number (ℝ≥0)

Distinct2495
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1125.80781
Minimum349
Maximum2910
Zeros0
Zeros (%)0.0%
Memory size501.3 KiB
2021-05-02T15:51:53.808583image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum349
5-th percentile471
Q1606
median1034
Q31390
95-th percentile2356
Maximum2910
Range2561
Interquartile range (IQR)784

Descriptive statistics

Standard deviation597.2644043
Coefficient of variation (CV)0.5305207506
Kurtosis-0.1475933883
Mean1125.80781
Median Absolute Deviation (MAD)419
Skewness0.885943425
Sum72220571
Variance356724.7686
MonotocityNot monotonic
2021-05-02T15:51:53.929418image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
568195
 
0.3%
537164
 
0.3%
538158
 
0.2%
540157
 
0.2%
546150
 
0.2%
528139
 
0.2%
539139
 
0.2%
536138
 
0.2%
634137
 
0.2%
533134
 
0.2%
Other values (2485)62639
97.6%
ValueCountFrequency (%)
3491
 
< 0.1%
3502
< 0.1%
3511
 
< 0.1%
3521
 
< 0.1%
3533
< 0.1%
ValueCountFrequency (%)
29102
< 0.1%
29041
< 0.1%
28881
< 0.1%
28791
< 0.1%
28721
< 0.1%

VDE
Real number (ℝ)

Distinct2874
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean343.0272954
Minimum-1
Maximum4383
Zeros105
Zeros (%)0.2%
Memory size501.3 KiB
2021-05-02T15:51:54.047859image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile19
Q186
median191
Q3342
95-th percentile1366
Maximum4383
Range4384
Interquartile range (IQR)256

Descriptive statistics

Standard deviation491.5684643
Coefficient of variation (CV)1.433030172
Kurtosis11.88432141
Mean343.0272954
Median Absolute Deviation (MAD)116
Skewness3.203941913
Sum22005201
Variance241639.5551
MonotocityNot monotonic
2021-05-02T15:51:54.162222image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36236
 
0.4%
49234
 
0.4%
20232
 
0.4%
21227
 
0.4%
34215
 
0.3%
44215
 
0.3%
59214
 
0.3%
25213
 
0.3%
33212
 
0.3%
54212
 
0.3%
Other values (2864)61940
96.6%
ValueCountFrequency (%)
-148
 
0.1%
0105
0.2%
1104
0.2%
2123
0.2%
3129
0.2%
ValueCountFrequency (%)
43831
< 0.1%
40261
< 0.1%
39241
< 0.1%
38821
< 0.1%
38711
< 0.1%

TES
Real number (ℝ≥0)

Distinct9737
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6144.674778
Minimum2064
Maximum17967
Zeros0
Zeros (%)0.0%
Memory size501.3 KiB
2021-05-02T15:51:54.280198image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum2064
5-th percentile3358
Q14637
median5801
Q37266
95-th percentile10234
Maximum17967
Range15903
Interquartile range (IQR)2629

Descriptive statistics

Standard deviation2113.558691
Coefficient of variation (CV)0.3439659164
Kurtosis1.648975109
Mean6144.674778
Median Absolute Deviation (MAD)1283
Skewness1.026298519
Sum394180887
Variance4467130.339
MonotocityNot monotonic
2021-05-02T15:51:54.395295image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
568228
 
< 0.1%
512128
 
< 0.1%
542927
 
< 0.1%
560826
 
< 0.1%
506125
 
< 0.1%
501725
 
< 0.1%
540025
 
< 0.1%
505824
 
< 0.1%
531224
 
< 0.1%
556024
 
< 0.1%
Other values (9727)63894
99.6%
ValueCountFrequency (%)
20641
< 0.1%
20691
< 0.1%
20701
< 0.1%
20811
< 0.1%
21011
< 0.1%
ValueCountFrequency (%)
179671
< 0.1%
179551
< 0.1%
178701
< 0.1%
176901
< 0.1%
176581
< 0.1%

GES
Real number (ℝ≥0)

Distinct2940
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean843.2490725
Minimum40
Maximum3695
Zeros0
Zeros (%)0.0%
Memory size501.3 KiB
2021-05-02T15:51:54.520718image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile71
Q1324
median715
Q31237
95-th percentile2064
Maximum3695
Range3655
Interquartile range (IQR)913

Descriptive statistics

Standard deviation630.4036642
Coefficient of variation (CV)0.7475889209
Kurtosis0.2446437739
Mean843.2490725
Median Absolute Deviation (MAD)436
Skewness0.8657316114
Sum54094428
Variance397408.7798
MonotocityNot monotonic
2021-05-02T15:51:54.635412image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66268
 
0.4%
68196
 
0.3%
64190
 
0.3%
60189
 
0.3%
69186
 
0.3%
65175
 
0.3%
67172
 
0.3%
70137
 
0.2%
72133
 
0.2%
75130
 
0.2%
Other values (2930)62374
97.2%
ValueCountFrequency (%)
405
 
< 0.1%
4111
 
< 0.1%
4229
< 0.1%
4362
0.1%
4461
0.1%
ValueCountFrequency (%)
36952
< 0.1%
36171
< 0.1%
35701
< 0.1%
35641
< 0.1%
35061
< 0.1%

GAES_GEN
Real number (ℝ≥0)

ZEROS

Distinct1165
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean162.7360561
Minimum0
Maximum1513
Zeros42114
Zeros (%)65.6%
Memory size501.3 KiB
2021-05-02T15:51:54.755608image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3321
95-th percentile786
Maximum1513
Range1513
Interquartile range (IQR)321

Descriptive statistics

Standard deviation270.7741951
Coefficient of variation (CV)1.663885691
Kurtosis2.656801204
Mean162.7360561
Median Absolute Deviation (MAD)0
Skewness1.775248653
Sum10439518
Variance73318.66475
MonotocityNot monotonic
2021-05-02T15:51:54.887734image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
042114
65.6%
3201839
 
2.9%
3231784
 
2.8%
3241610
 
2.5%
3221584
 
2.5%
3211397
 
2.2%
325822
 
1.3%
151408
 
0.6%
644379
 
0.6%
319352
 
0.5%
Other values (1155)11861
 
18.5%
ValueCountFrequency (%)
042114
65.6%
21
 
< 0.1%
32
 
< 0.1%
47
 
< 0.1%
64
 
< 0.1%
ValueCountFrequency (%)
15131
< 0.1%
15101
< 0.1%
15091
< 0.1%
14731
< 0.1%
14631
< 0.1%

CONSUMPTION
Real number (ℝ≥0)

Distinct12449
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17129.04971
Minimum10905
Maximum30727
Zeros0
Zeros (%)0.0%
Memory size501.3 KiB
2021-05-02T15:51:55.016034image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum10905
5-th percentile12711
Q115122
median16671.5
Q319174
95-th percentile22177
Maximum30727
Range19822
Interquartile range (IQR)4052

Descriptive statistics

Standard deviation2911.994458
Coefficient of variation (CV)0.1700032697
Kurtosis0.07259270441
Mean17129.04971
Median Absolute Deviation (MAD)1940.5
Skewness0.5165082332
Sum1098828539
Variance8479711.721
MonotocityNot monotonic
2021-05-02T15:51:55.134785image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1581023
 
< 0.1%
1598922
 
< 0.1%
1585922
 
< 0.1%
1593621
 
< 0.1%
1558221
 
< 0.1%
1604421
 
< 0.1%
1652720
 
< 0.1%
1545420
 
< 0.1%
1539220
 
< 0.1%
1637020
 
< 0.1%
Other values (12439)63940
99.7%
ValueCountFrequency (%)
109051
< 0.1%
109071
< 0.1%
110361
< 0.1%
111521
< 0.1%
111571
< 0.1%
ValueCountFrequency (%)
307272
< 0.1%
304901
< 0.1%
303511
< 0.1%
299161
< 0.1%
299041
< 0.1%

GAES_PUMP
Real number (ℝ)

ZEROS

Distinct1076
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-221.4053468
Minimum-1400
Maximum0
Zeros44839
Zeros (%)69.9%
Memory size501.3 KiB
2021-05-02T15:51:55.254135image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-1400
5-th percentile-1149
Q1-393
median0
Q30
95-th percentile0
Maximum0
Range1400
Interquartile range (IQR)393

Descriptive statistics

Standard deviation394.3763332
Coefficient of variation (CV)-1.781241234
Kurtosis0.6762130996
Mean-221.4053468
Median Absolute Deviation (MAD)0
Skewness-1.498530858
Sum-14203153
Variance155532.6922
MonotocityNot monotonic
2021-05-02T15:51:55.367306image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
044839
69.9%
-92304
 
0.5%
-707239
 
0.4%
-87220
 
0.3%
-703212
 
0.3%
-488194
 
0.3%
-8189
 
0.3%
-219183
 
0.3%
-1156150
 
0.2%
-1160141
 
0.2%
Other values (1066)17479
 
27.2%
ValueCountFrequency (%)
-14001
< 0.1%
-13981
< 0.1%
-13921
< 0.1%
-13912
< 0.1%
-13901
< 0.1%
ValueCountFrequency (%)
044839
69.9%
-184
 
0.1%
-217
 
< 0.1%
-3100
 
0.2%
-482
 
0.1%

UK_BLR_RUS
Real number (ℝ)

Distinct2296
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-17.59382697
Minimum-1185
Maximum1928
Zeros195
Zeros (%)0.3%
Memory size501.3 KiB
2021-05-02T15:51:55.486320image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-1185
5-th percentile-414
Q1-123
median-36
Q352
95-th percentile467.55
Maximum1928
Range3113
Interquartile range (IQR)175

Descriptive statistics

Standard deviation271.1646968
Coefficient of variation (CV)-15.41249083
Kurtosis5.535145492
Mean-17.59382697
Median Absolute Deviation (MAD)88
Skewness1.153942158
Sum-1128644
Variance73530.2928
MonotocityNot monotonic
2021-05-02T15:51:55.599955image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-39260
 
0.4%
-35250
 
0.4%
-25247
 
0.4%
-23245
 
0.4%
-41241
 
0.4%
-30239
 
0.4%
-19238
 
0.4%
-42238
 
0.4%
-51237
 
0.4%
-49235
 
0.4%
Other values (2286)61720
96.2%
ValueCountFrequency (%)
-11851
< 0.1%
-11551
< 0.1%
-11521
< 0.1%
-11371
< 0.1%
-11251
< 0.1%
ValueCountFrequency (%)
19281
< 0.1%
19021
< 0.1%
17441
< 0.1%
17381
< 0.1%
16931
< 0.1%

UK_EURO
Real number (ℝ)

Distinct1286
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-452.9123928
Minimum-926
Maximum427
Zeros10
Zeros (%)< 0.1%
Memory size501.3 KiB
2021-05-02T15:51:55.715106image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-926
5-th percentile-814
Q1-622
median-463
Q3-298
95-th percentile-94
Maximum427
Range1353
Interquartile range (IQR)324

Descriptive statistics

Standard deviation228.3948625
Coefficient of variation (CV)-0.5042804439
Kurtosis0.04218481976
Mean-452.9123928
Median Absolute Deviation (MAD)162
Skewness0.3595837202
Sum-29054330
Variance52164.21322
MonotocityNot monotonic
2021-05-02T15:51:55.842155image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-472139
 
0.2%
-480138
 
0.2%
-482138
 
0.2%
-353138
 
0.2%
-476137
 
0.2%
-346136
 
0.2%
-478136
 
0.2%
-468135
 
0.2%
-486134
 
0.2%
-344134
 
0.2%
Other values (1276)62785
97.9%
ValueCountFrequency (%)
-9261
< 0.1%
-9251
< 0.1%
-9221
< 0.1%
-9171
< 0.1%
-9161
< 0.1%
ValueCountFrequency (%)
4271
< 0.1%
4211
< 0.1%
4161
< 0.1%
4111
< 0.1%
4061
< 0.1%

UK_MLD
Real number (ℝ)

Distinct744
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-53.5909431
Minimum-687
Maximum377
Zeros455
Zeros (%)0.7%
Memory size501.3 KiB
2021-05-02T15:51:55.962731image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-687
5-th percentile-182
Q1-95
median-38
Q33
95-th percentile48
Maximum377
Range1064
Interquartile range (IQR)98

Descriptive statistics

Standard deviation86.50529366
Coefficient of variation (CV)-1.614177483
Kurtosis7.454736773
Mean-53.5909431
Median Absolute Deviation (MAD)46
Skewness-1.987228732
Sum-3437859
Variance7483.165832
MonotocityNot monotonic
2021-05-02T15:51:56.079823image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1467
 
0.7%
2457
 
0.7%
0455
 
0.7%
-5455
 
0.7%
6454
 
0.7%
3452
 
0.7%
-4451
 
0.7%
-2450
 
0.7%
5446
 
0.7%
4442
 
0.7%
Other values (734)59621
92.9%
ValueCountFrequency (%)
-6871
< 0.1%
-6721
< 0.1%
-6711
< 0.1%
-6531
< 0.1%
-6421
< 0.1%
ValueCountFrequency (%)
3771
< 0.1%
3141
< 0.1%
2181
< 0.1%
1921
< 0.1%
1901
< 0.1%

Interactions

2021-05-02T15:51:33.769728image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:33.972482image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:34.150288image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:34.339658image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:34.523315image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:34.711281image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:34.902117image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:35.096589image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:35.281917image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:35.462655image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:35.636565image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:35.811272image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:35.972505image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:36.148458image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:36.320580image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:36.496217image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:36.673142image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:36.847545image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:37.014356image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:37.184192image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:37.349633image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:37.515540image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:37.675652image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:37.846028image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:38.008348image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:38.171182image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:38.343494image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:38.509585image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:38.670254image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:38.830000image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:38.985453image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:39.170192image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:39.348108image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:39.527927image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:39.711459image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:39.896019image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:40.093010image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:40.281330image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:40.467464image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:40.649073image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:40.826048image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:41.006035image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:41.195475image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:41.366899image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:41.554343image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:41.736443image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:41.926321image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:42.109007image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:42.285008image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:42.461913image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:42.636012image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:42.813782image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:42.984025image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:43.153023image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:43.335566image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:43.513746image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:43.702192image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:43.882938image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:44.059961image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:44.238736image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:44.414335image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:44.605696image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:44.788324image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:44.975756image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:45.175315image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:45.361817image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:45.551190image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:45.743484image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:45.930067image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:46.115128image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:46.295401image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:46.475616image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:46.648180image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:46.818420image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:47.003964image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:47.182334image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:47.365876image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:47.557277image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:47.735422image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:47.913913image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:48.085405image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:48.259434image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:48.429604image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:48.595780image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:48.774993image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:48.951373image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:49.128761image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:49.310401image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:49.489373image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:49.661068image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:49.830153image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:50.011428image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:50.180055image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:50.345838image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:50.527758image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:50.701840image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:50.877147image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:51.060262image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:51.239140image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:51.410961image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:51.579352image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:51.735204image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:51.832050image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:51.926069image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:52.029623image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:52.133804image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:52.234739image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:52.338981image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:52.441214image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-02T15:51:52.540080image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Correlations

2021-05-02T15:51:56.184461image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-02T15:51:56.324824image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-02T15:51:56.463876image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-02T15:51:56.602144image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-05-02T15:51:52.752290image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
A simple visualization of nullity by column.
2021-05-02T15:51:53.033526image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

datereportAESTECVDETESGESGAES_GENCONSUMPTIONGAES_PUMPUK_BLR_RUSUK_EUROUK_MLD
02021-04-28 07:00:0010303.01126.0630.03379.01902.00.016820.00.0-42.0-413.0-65.0
12021-04-28 06:00:0010304.0963.0396.03068.01098.00.015271.0-87.0-35.0-444.08.0
22021-04-28 05:00:0010263.0938.0386.03029.0923.00.014498.0-488.0-86.0-467.00.0
32021-04-28 04:00:0010229.0944.0357.03037.01162.00.014514.0-705.0-84.0-448.022.0
42021-04-28 03:00:0010113.0939.0436.03045.01264.00.014468.0-710.0-94.0-463.0-62.0
52021-04-28 02:00:0010250.0977.0406.02983.0921.00.014650.0-306.0-86.0-450.0-45.0
62021-04-28 01:00:0010242.01065.0384.03183.0918.00.015297.0-13.0-40.0-448.06.0
72021-04-28 00:00:0010273.01135.0355.03747.0930.040.016117.00.070.0-400.0-33.0
82021-04-27 23:00:0010509.01255.0322.03815.02147.0630.017794.00.0-568.0-358.042.0
92021-04-27 22:00:0010644.01260.0375.03965.02238.0562.018744.00.022.0-300.0-22.0

Last rows

datereportAESTECVDETESGESGAES_GENCONSUMPTIONGAES_PUMPUK_BLR_RUSUK_EUROUK_MLD
641402014-01-01 10:00:0010455.02020.0118.08303.0862.00.019014.0-88.0-120.0-405.0-76.0
641412014-01-01 09:00:0010479.02017.0113.08233.0557.00.018276.0-480.0-123.0-402.0-69.0
641422014-01-01 08:00:0010493.02013.054.08179.072.00.017480.0-703.0-173.0-399.0-17.0
641432014-01-01 07:00:0010469.02013.055.08202.0193.00.017565.0-707.0-200.0-410.0-11.0
641442014-01-01 06:00:0010473.02013.055.08201.0434.00.017911.0-707.0-93.0-416.0-8.0
641452014-01-01 05:00:0010427.02009.049.08355.0274.00.018072.0-488.0-90.0-421.0-11.0
641462014-01-01 04:00:0010475.02014.042.08369.0185.00.018453.00.0-175.0-399.0-26.0
641472014-01-01 03:00:0010515.02022.054.08731.0355.00.019107.00.0-59.0-400.0-60.0
641482014-01-01 02:00:0010606.02014.049.08885.0863.00.019665.00.0-200.0-409.0-104.0
641492014-01-01 01:00:0010728.02016.050.08892.01809.00.020586.00.0-365.0-355.0-150.0